One goal of a task-based imaging system may be to provide task-specific information or image data for one or more signal-processing tasks. Such tasks may include biometric iris recognition, biometric face recognition, biometric recognition for access control, biometric recognition for threat identification, barcode reading, imaging for quality control in an assembly line, optical character recognition, biological imaging, automotive imaging for object detection and fiducial mark recognition for registration of objects during automated assembly. The above-mentioned biometric recognition tasks, for instance, may be executed by task-based imaging systems for security or access purposes. As an example, biometric iris recognition can provide human identification with very high accuracy when optical and digital portions of such a task-based imaging system provide image data that is detailed enough and has a high enough signal-to-noise ratio (“SNR”).
The performance of a task-based imaging system is known to be directly related to an SNR of image data that is required for successful completion of the task. The SNR is in turn related to the characteristics of the imaging system. Characteristics that affect system performance include spherical and other aberrations, defocus, variations in magnification, depth of field, chromatic aberration, alignment tolerances, dynamic vibrations and temperature variations. These characteristics can cause the system to have a task-specific SNR that is smaller than that of a diffraction-limited system.
Certain systems described in the prior art perform iris recognition at short distances, using small apertures; see, for example, R. Plemmons et al., “Computational imaging systems for iris recognition,” in Proc. SPIE, August 2004. However, while such systems are effective for short standoff distances, they may use small lens apertures, which lead to low signal levels (i.e., low SNR) and relatively low resolution; such systems may not be suitable for longer standoff distances.